The invention relates to an adaptive filter, and in particular, to the adaptive filter having a hybrid structure and providing a tri-mode operation.
Adaptive filtering is a digital signal processing technique that has been widely used in numerous signal processing applications such as echo cancellation, noise cancellation, channel equalization and system identification. Characteristics of an adaptive filter are largely determined by its adaptation algorithm, and the choice of the adaptation algorithm in a specific adaptive filtering system directly affects the performance of the system.
Being simple and easily stable, the normalized least mean square (NLMS) adaptation algorithm is now widely used in the industry with a certain degree of success. However, because of its intrinsic weakness, this algorithm converges slowly, especially with colored training signals, e.g. speech signals. As a result, the performance of systems incorporating NLMS adaptive filters often suffers from the slow convergence nature of the algorithm.
To solve the problem of slow convergence, it has been proposed to use a modification of an Affine Projection algorithm which is known as Fast Affine Projection (FAP) algorithm, see e.g. publication by Steven L. Gay and Sanjeev Tavathia (Acoustic Research Department, ATandT Bell Laboratories), xe2x80x9cThe Fast Affine Projection Algorithm,xe2x80x9d pp. 3023-3026, Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, May 1995, Detroit, Mich., U.S.A. and U.S. Pat. No. #5,428,562 to Gay. The FAP converges several times faster than NLMS, with only a marginal increase in implementation complexity. However, a stability issue has been preventing FAP from being used in the industry.
A remedy for the instability problem associated with the FAP algorithm has been proposed in two U.S. patent applications to Heping Ding Ser. Nos. 09/218,428 and 09/356,041 filed Dec. 22, 1998 and Jul. 16, 1999 respectively, wherein a new Stable FAP (SFAP) filter and method have been invented. The cited patent applications are incorporated herein by reference. The SFAP filter solves the instability problem of FAP caused by error accumulation in an inversion process of an auto-correlation matrix while providing the advantages of other known methods.
Although the development of fast convergence adaptive methods may be considered as a giant leap forward, there is still a problem remaining which is common to all existing adaptive filters while most severely influencing the performance of the fast convergence adaptive filters. The problem is that the adaptive filter performs satisfactory only in the absence of the near-end signal or when the near end signal is negligibly small.
To describe this problem in more detail, let us refer to FIG. 1, which illustrates a typical echo-cancellation system 10 with an embedded adaptive filter 100. A digitallysampled far-end reference input signal x(n) is supplied to the adaptive filter 100 and to an echo path 14 producing an unwanted signal u(n), the signal being an echo of the input signal x(n) through the echo path 14. The unwanted signal u(n) is mixed up with the wanted near-end signal s(n) in a summer 16 to produce a response signal d(n). The response signal d(n) is sent to another summer 18 together with an echo estimate signal y(n) generated by the adaptive filter 100. The summer 18 subtracts y(n) from d(n) producing an output signal e(n) to be transmitted to the far-end. The echo path 14 can be constantly changing, and the adaptive filter 100 must continuously adapt to the new echo path. Therefore the echo estimate signal y(n) must be as close to u(n) as possible, so that the latter is largely cancelled by the former to ensure that e(n) best resembles s(n). The output signal e(n), called the error signal, is then transmitted to the far-end and at the same time sent to the adaptive filter 100 which uses the signal to adjust the filter coefficients.
If the near-end signal s(n) is not small enough to ensure that the response signal d(n) is purely an echo signal u(n), the response signal d(n) contains components of the near end signal s(n) which can be short-term correlated with x(n). This is because both x(n) and s(n) are speech signals, which often show short-term correlations with each other even if they come from different sources. As a result, the adaptive filter 100 may mis-converge, and the near-end signal s(n) component in e(n) may be distorted, because the adaptive filter attempts to track and cancel components in the near-end signal s(n) that are short-term correlated to the input far-end signal x(n). The faster the convergence of the adaptive filter""s adaptation algorithm, the stronger the influence of the near end signal s(n) on the performance of the filter operation, causing the filter to mis-converge and to distort the near-end signal itself in the output e(n).
Accordingly, there is a need for development of an adaptive filter and method which would provide high performance operation in the presence of the near-end signal.
It is therefore an object of the invention to provide an adaptive filter and a method of adaptive filtering which would avoid the afore-mentioned problems.
According to one aspect of the present invention there is provided a method of adaptive filtering, comprising the steps of:
providing operation of an adaptive filter in one of the three modes, the modes being a fast converging mode, a slow converging mode and a freezing mode;
measuring non-convergence of the filter and a near-end signal, and generating a feedback signal based on the measurements; and
switching between the modes of operation of the filter in response to the feedback signal.
Convenietly, the step of measuring non-convergence and near-end signal comprises comparing the measured values with predetermined threshold values for non-convergence and near end signals respectively.
The step of switching between the modes of operation of the filter comprises switching the adaptive filter to the freezing mode when the near-end signal is above the corresponding threshold value, and non-convergence is below the corresponding threshold value. Alternatively, when non-convergence is above the threshold value and the near-end signal is below the threshold value, the filter is switched to the fast converging mode. Yet alternatively, when non-convergence and near-end signal are both below the corresponding threshold values, the adaptive filter is switched to the slow converging mode. When the non-convergence and near-end signal are both above the corresponding threshold values, the filter may be switched to either one of the three modes depending on the system requirements.
Advantageously, operation of the adaptive filter in the slow converging mode comprises adaptive filtering in accordance with a normalized least mean square (NLMS) method, and operation of the filter in the fast converging mode comprises providing adaptive filtering in accordance with a fast affine projection (FAP) method or Stable fast affine projection method (SFAP). Beneficially, the adaptive filtering in accordance with the SFAP method comprises using a steepest descent method or a conjugate gradient method.
According to another aspect of the invention there is provided a method of adaptive filtering, comprising the steps of:
(a) providing initial adaptive filtering for an incoming signal by one of adaptive filtering methods;
(b) measuring non-convergence of the adaptive filtering method and a near-end signal;
(c) comparing the measured values of the non-convergence and near-end signal with pre-determined threshold values;
(d) when the near-end signal is above the corresponding threshold value and non-convergence is below the corresponding threshold value, freezing coefficients in the currently used adaptive filtering method;
(e) when non-convergence is above the corresponding threshold value and the near-end signal is below the corresponding threshold value, providing adaptive filtering in accordance with a fast converging adaptive filtering method;
(f) when non-convergence and near-end signal are both below the corresponding threshold values, providing adaptive filtering in accordance with a slow converging adaptive filtering method; and
(g) repeating the steps (b) to (f) required number of times.
The method may comprise an additional step of providing adaptive filtering in accordance with my one of the three adaptive modes, a fast converging mode, a slow converging mode, or freezing mode, when the non-convergence and near-end signal are both above the corresponding threshold values, the step being performed before the step (g). In the freezing mode, the adaptive filter coefficients are not being updated, i.e. frozen, and the filter performs convolution only without adaptation.
Advantageously, the step of providing adaptive filtering in accordance with the slow converging adaptive filtering method comprises providing adaptive filtering in accordance with a normalized least mean square (NLMS) method, and the step of providing adaptive filtering in accordance with the fast converging adaptive filtering method comprises providing adaptive filtering in accordance with fast affine projection (FAP) method or stable fast affine projection (SFAP) method. Beneficially, the adaptive filtering in accordance with the SFAP method comprises using a conjugate gradient method. Alternatively, the adaptive filtering in accordance with the SFAP method may comprise using a steepest descent method.
According to yet another aspect of the invention there is provided an adaptive filter, comprising:
means for providing operation of the adaptive filter in one of the three modes, the modes including fast converging mode, slow converging mode and a freezing mode;
means for measuring non-convergence of the filter and a near-end signal, and generating a feedback signal; and
means for switching between the modes of operation of the filter in response to the feedback signal.
Conveniently, the means for providing operation of the adaptive filter in one of the three modes comprises first, second and third sub-filters which operate in fast converging mode, slow converging mode, and freezing mode respectively. Means for measuring non-convergence and near-end signal comprises a quality detector which identifies presence of non-convergence (NC) and near-end signal (NS), distinguishes between them and compares the measured values with pre-determined threshold values.
Advantageously, the first sub-filter comprises a fast affine projection (FAP) filter or a stable fast affine projection filter (SFAP), the SFAP filter including one of the steepest descent and conjugate gradient calculator, operating in accordance with the steepest descent and conjugate gradient methods respectively.
Beneficially, the second sub-filter comprises a normalized least mean square (NLMS) filter, and the third sub-filter comprises a filter which operates without updating the filter coefficients, thereby providing convolution only.
The adaptive filter and method described above have an advantage over known adaptive filters by providing quality operation in the presence of the near-end signal and combining advantages of existing adaptive filtering techniques while avoiding their shortcomings.